Matching Items (222)
Description
Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
As obesity rates continue to rise in adolescents and young children, the concern for poor future health of the younger population grows. Physical activity and improving nutrition are two ways to combat obesity rates, and the Sustainability via Active Gardening Education (SAGE) project addresses this in underserved and low-income communities in Maricopa County. This project employs a curriculum designed to promote physical activity and healthy eating for Early Care and Education (ECE) sites, most of which are daycares. Further, utilizing indicators of future health can also allow for us to understand and lower obesity rates. One indicator of future health is grip strength: greater grip strength is associated with healthier outcomes such as lower triglycerides, blood pressure, and body mass index. Grip strength has been observed in the older population; however, there are few studies looking at grip strength in younger children, namely preschoolers. As grip strength is a predictor of health, it follows that it should be observed in preschoolers, and improved, if possible, by factors such as physical activity, which would ultimately improve obesity rates. This study aimed to see if there was any relationship between physical activity and grip strength in preschoolers aged 3-5 years old. To do so, grip strength, hand length, height, weight, and information regarding physical activity of preschoolers enrolled in the SAGE project were collected.
Physical activity and grip strength were not found to be significantly associated in this study; however, hand length and hand strength were associated. Among secondary outcomes, it was observed that males of ages 3 to 5-years-old may have greater hand grip strength than females of the same age group. Although this was not statistically significant, there was a trend toward statistical significance. Small sample size hampered observation of expected relationships between hand grip strength and dominant hand of the participants, and hand grip strength was not significantly related with BMI. Future directions would consist of collecting longitudinal data, as well as calling back previous years’ participants for additional data, so that there is a larger sample size for data analysis.
ContributorsAtluri, Haarika (Author) / Lee, Rebecca (Thesis director) / Tucker, Derek (Committee member) / Cantu Garcia, Lisbeth (Committee member) / De Mello, Gabrielli (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2024-05